Overview

Dataset statistics

Number of variables14
Number of observations2777
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory303.9 KiB
Average record size in memory112.0 B

Variable types

Numeric14

Alerts

gross_revenue is highly correlated with qnt_purchases and 3 other fieldsHigh correlation
qnt_purchases is highly correlated with gross_revenue and 2 other fieldsHigh correlation
var_products is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qnt_items is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly correlated with avg_basket_varietyHigh correlation
avg_recency_days is highly correlated with freq_purchaseHigh correlation
freq_purchase is highly correlated with avg_recency_daysHigh correlation
qtd_returned is highly correlated with freq_returnsHigh correlation
freq_returns is highly correlated with qtd_returnedHigh correlation
avg_basket_size is highly correlated with gross_revenue and 1 other fieldsHigh correlation
avg_basket_variety is highly correlated with var_products and 1 other fieldsHigh correlation
gross_revenue is highly correlated with qnt_purchases and 1 other fieldsHigh correlation
qnt_purchases is highly correlated with gross_revenue and 2 other fieldsHigh correlation
var_products is highly correlated with qnt_purchasesHigh correlation
qnt_items is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_ticket is highly correlated with qtd_returned and 1 other fieldsHigh correlation
qtd_returned is highly correlated with avg_ticketHigh correlation
avg_basket_size is highly correlated with qnt_items and 1 other fieldsHigh correlation
gross_revenue is highly correlated with qnt_purchases and 2 other fieldsHigh correlation
qnt_purchases is highly correlated with gross_revenue and 2 other fieldsHigh correlation
var_products is highly correlated with gross_revenue and 2 other fieldsHigh correlation
qnt_items is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_recency_days is highly correlated with freq_purchaseHigh correlation
freq_purchase is highly correlated with avg_recency_daysHigh correlation
qtd_returned is highly correlated with freq_returnsHigh correlation
freq_returns is highly correlated with qtd_returnedHigh correlation
avg_basket_size is highly correlated with qnt_itemsHigh correlation
df_index is highly correlated with avg_recency_daysHigh correlation
gross_revenue is highly correlated with qnt_purchases and 4 other fieldsHigh correlation
qnt_purchases is highly correlated with gross_revenue and 4 other fieldsHigh correlation
var_products is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qnt_items is highly correlated with gross_revenue and 4 other fieldsHigh correlation
avg_ticket is highly correlated with qtd_returned and 1 other fieldsHigh correlation
avg_recency_days is highly correlated with df_indexHigh correlation
qtd_returned is highly correlated with gross_revenue and 5 other fieldsHigh correlation
avg_basket_size is highly correlated with gross_revenue and 4 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 27.69775288) Skewed
freq_purchase is highly skewed (γ1 = 46.11035601) Skewed
qtd_returned is highly skewed (γ1 = 21.64144035) Skewed
df_index has unique values Unique
customer_id has unique values Unique
recency_days has 33 (1.2%) zeros Zeros
qtd_returned has 1484 (53.4%) zeros Zeros
freq_returns has 1484 (53.4%) zeros Zeros

Reproduction

Analysis started2021-10-18 18:23:49.273994
Analysis finished2021-10-18 18:24:09.594087
Duration20.32 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct2777
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2253.89197
Minimum0
Maximum5705
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-18T15:24:09.675439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile181.8
Q1903
median2063
Q33415
95-th percentile4964.2
Maximum5705
Range5705
Interquartile range (IQR)2512

Descriptive statistics

Standard deviation1528.418125
Coefficient of variation (CV)0.6781239498
Kurtosis-0.9554136096
Mean2253.89197
Median Absolute Deviation (MAD)1242
Skewness0.3797900471
Sum6259058
Variance2336061.965
MonotonicityStrictly increasing
2021-10-18T15:24:09.776204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
29121
 
< 0.1%
28981
 
< 0.1%
29011
 
< 0.1%
29021
 
< 0.1%
29061
 
< 0.1%
29071
 
< 0.1%
29081
 
< 0.1%
29091
 
< 0.1%
29111
 
< 0.1%
Other values (2767)2767
99.6%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
57051
< 0.1%
56951
< 0.1%
56891
< 0.1%
56641
< 0.1%
56581
< 0.1%
56471
< 0.1%
56461
< 0.1%
56291
< 0.1%
56281
< 0.1%
56191
< 0.1%

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct2777
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15284.17033
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-18T15:24:09.877410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12625.8
Q113815
median15240
Q316779
95-th percentile17950.2
Maximum18287
Range5940
Interquartile range (IQR)2964

Descriptive statistics

Standard deviation1715.038366
Coefficient of variation (CV)0.1122101055
Kurtosis-1.205963417
Mean15284.17033
Median Absolute Deviation (MAD)1483
Skewness0.0167561014
Sum42444141
Variance2941356.595
MonotonicityNot monotonic
2021-10-18T15:24:09.973437image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
141631
 
< 0.1%
177041
 
< 0.1%
169331
 
< 0.1%
137721
 
< 0.1%
162491
 
< 0.1%
141981
 
< 0.1%
139891
 
< 0.1%
179301
 
< 0.1%
144821
 
< 0.1%
Other values (2767)2767
99.6%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123521
< 0.1%
123561
< 0.1%
123581
< 0.1%
123591
< 0.1%
123601
< 0.1%
123621
< 0.1%
123631
< 0.1%
123641
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182731
< 0.1%
182721
< 0.1%
182701
< 0.1%
182651
< 0.1%
182631
< 0.1%
182611
< 0.1%
182601
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2763
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2842.127526
Minimum36.56
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-18T15:24:10.073358image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum36.56
5-th percentile264.584
Q1627.13
median1166.77
Q32420.84
95-th percentile7467.416
Maximum279138.02
Range279101.46
Interquartile range (IQR)1793.71

Descriptive statistics

Standard deviation10459.57225
Coefficient of variation (CV)3.680191038
Kurtosis373.3049261
Mean2842.127526
Median Absolute Deviation (MAD)684.76
Skewness17.10907554
Sum7892588.14
Variance109402651.7
MonotonicityNot monotonic
2021-10-18T15:24:10.169495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1025.442
 
0.1%
1353.742
 
0.1%
745.062
 
0.1%
379.652
 
0.1%
734.942
 
0.1%
2092.322
 
0.1%
178.962
 
0.1%
889.932
 
0.1%
731.92
 
0.1%
2053.022
 
0.1%
Other values (2753)2757
99.3%
ValueCountFrequency (%)
36.561
< 0.1%
521
< 0.1%
52.21
< 0.1%
62.431
< 0.1%
68.841
< 0.1%
70.021
< 0.1%
77.41
< 0.1%
84.651
< 0.1%
90.31
< 0.1%
93.351
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
140450.721
< 0.1%
124564.531
< 0.1%
117379.631
< 0.1%
91062.381
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%
65039.621
< 0.1%

recency_days
Real number (ℝ≥0)

ZEROS

Distinct252
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.75693194
Minimum0
Maximum372
Zeros33
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-18T15:24:10.287858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q110
median29
Q373
95-th percentile211
Maximum372
Range372
Interquartile range (IQR)63

Descriptive statistics

Standard deviation68.4423255
Coefficient of variation (CV)1.20588487
Kurtosis3.405942763
Mean56.75693194
Median Absolute Deviation (MAD)24
Skewness1.891632142
Sum157614
Variance4684.35192
MonotonicityNot monotonic
2021-10-18T15:24:10.388952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
3.6%
487
 
3.1%
285
 
3.1%
385
 
3.1%
876
 
2.7%
1067
 
2.4%
966
 
2.4%
765
 
2.3%
1762
 
2.2%
2255
 
2.0%
Other values (242)2030
73.1%
ValueCountFrequency (%)
033
 
1.2%
199
3.6%
285
3.1%
385
3.1%
487
3.1%
543
1.5%
765
2.3%
876
2.7%
966
2.4%
1067
2.4%
ValueCountFrequency (%)
3721
 
< 0.1%
3661
 
< 0.1%
3601
 
< 0.1%
3583
0.1%
3541
 
< 0.1%
3371
 
< 0.1%
3362
0.1%
3341
 
< 0.1%
3332
0.1%
3301
 
< 0.1%

qnt_purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct55
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.049333813
Minimum2
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-18T15:24:10.494540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median4
Q36
95-th percentile17
Maximum206
Range204
Interquartile range (IQR)4

Descriptive statistics

Standard deviation9.067395776
Coefficient of variation (CV)1.498908153
Kurtosis184.1067332
Mean6.049333813
Median Absolute Deviation (MAD)2
Skewness10.62910516
Sum16799
Variance82.21766616
MonotonicityNot monotonic
2021-10-18T15:24:10.604850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2782
28.2%
3500
18.0%
4393
14.2%
5237
 
8.5%
6173
 
6.2%
7138
 
5.0%
898
 
3.5%
969
 
2.5%
1055
 
2.0%
1154
 
1.9%
Other values (45)278
 
10.0%
ValueCountFrequency (%)
2782
28.2%
3500
18.0%
4393
14.2%
5237
 
8.5%
6173
 
6.2%
7138
 
5.0%
898
 
3.5%
969
 
2.5%
1055
 
2.0%
1154
 
1.9%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
0.1%
861
< 0.1%
721
< 0.1%
622
0.1%
601
< 0.1%
571
< 0.1%

var_products
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct467
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.6622254
Minimum2
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-18T15:24:10.713364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q134
median72
Q3143
95-th percentile399.6
Maximum7838
Range7836
Interquartile range (IQR)109

Descriptive statistics

Standard deviation277.6455126
Coefficient of variation (CV)2.141298375
Kurtosis337.1576638
Mean129.6622254
Median Absolute Deviation (MAD)45
Skewness15.35610984
Sum360072
Variance77087.03068
MonotonicityNot monotonic
2021-10-18T15:24:11.045161image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2838
 
1.4%
3534
 
1.2%
2730
 
1.1%
2630
 
1.1%
2930
 
1.1%
3128
 
1.0%
1527
 
1.0%
1927
 
1.0%
2527
 
1.0%
3326
 
0.9%
Other values (457)2480
89.3%
ValueCountFrequency (%)
211
0.4%
312
0.4%
416
0.6%
516
0.6%
624
0.9%
714
0.5%
813
0.5%
919
0.7%
1019
0.7%
1123
0.8%
ValueCountFrequency (%)
78381
< 0.1%
56731
< 0.1%
50951
< 0.1%
45801
< 0.1%
26981
< 0.1%
23791
< 0.1%
20601
< 0.1%
18181
< 0.1%
16731
< 0.1%
16371
< 0.1%

qnt_items
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1638
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1670.081743
Minimum2
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-18T15:24:11.146570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile119.8
Q1330
median703
Q31478
95-th percentile4607
Maximum196844
Range196842
Interquartile range (IQR)1148

Descriptive statistics

Standard deviation5886.62708
Coefficient of variation (CV)3.524753866
Kurtosis486.1295226
Mean1670.081743
Median Absolute Deviation (MAD)451
Skewness18.18931357
Sum4637817
Variance34652378.38
MonotonicityNot monotonic
2021-10-18T15:24:11.248617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31011
 
0.4%
2468
 
0.3%
1508
 
0.3%
3007
 
0.3%
2197
 
0.3%
4937
 
0.3%
2607
 
0.3%
12007
 
0.3%
2727
 
0.3%
5167
 
0.3%
Other values (1628)2701
97.3%
ValueCountFrequency (%)
21
< 0.1%
161
< 0.1%
171
< 0.1%
191
< 0.1%
201
< 0.1%
251
< 0.1%
272
0.1%
301
< 0.1%
321
< 0.1%
332
0.1%
ValueCountFrequency (%)
1968441
< 0.1%
802631
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
633121
< 0.1%
583431
< 0.1%
578851
< 0.1%
502551
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct2775
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.08667366
Minimum2.150588235
Maximum4453.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-18T15:24:11.352051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.150588235
5-th percentile4.853447184
Q112.44512195
median17.94344444
Q324.99947917
95-th percentile87.58663158
Maximum4453.43
Range4451.279412
Interquartile range (IQR)12.55435722

Descriptive statistics

Standard deviation107.5551384
Coefficient of variation (CV)3.352018959
Kurtosis1056.119771
Mean32.08667366
Median Absolute Deviation (MAD)6.312279396
Skewness27.69775288
Sum89104.69275
Variance11568.1078
MonotonicityNot monotonic
2021-10-18T15:24:11.446424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.1622
 
0.1%
14.478333332
 
0.1%
18.152222221
 
< 0.1%
30.881
 
< 0.1%
32.597751
 
< 0.1%
19.030483871
 
< 0.1%
28.554516131
 
< 0.1%
12.800681821
 
< 0.1%
6.3962146891
 
< 0.1%
26.087971011
 
< 0.1%
Other values (2765)2765
99.6%
ValueCountFrequency (%)
2.1505882351
< 0.1%
2.43251
< 0.1%
2.4623711341
< 0.1%
2.5112413791
< 0.1%
2.5153333331
< 0.1%
2.651
< 0.1%
2.6569318181
< 0.1%
2.7075982531
< 0.1%
2.7606215721
< 0.1%
2.7704641911
< 0.1%
ValueCountFrequency (%)
4453.431
< 0.1%
1687.21
< 0.1%
952.98751
< 0.1%
872.131
< 0.1%
841.02144931
< 0.1%
651.16833331
< 0.1%
6401
< 0.1%
624.41
< 0.1%
615.751
< 0.1%
602.45313231
< 0.1%

avg_recency_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1155
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.72774209
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-18T15:24:11.542451image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q134.22222222
median59
Q399
95-th percentile224
Maximum366
Range365
Interquartile range (IQR)64.77777778

Descriptive statistics

Standard deviation66.46046693
Coefficient of variation (CV)0.8441810366
Kurtosis3.693037322
Mean78.72774209
Median Absolute Deviation (MAD)30
Skewness1.831604949
Sum218626.9398
Variance4416.993664
MonotonicityNot monotonic
2021-10-18T15:24:11.644289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7021
 
0.8%
4618
 
0.6%
5517
 
0.6%
4916
 
0.6%
3116
 
0.6%
9116
 
0.6%
2115
 
0.5%
3515
 
0.5%
4215
 
0.5%
2614
 
0.5%
Other values (1145)2614
94.1%
ValueCountFrequency (%)
19
0.3%
24
0.1%
2.8615384621
 
< 0.1%
36
0.2%
3.3303571431
 
< 0.1%
3.3513513511
 
< 0.1%
45
0.2%
4.1910112361
 
< 0.1%
4.2758620691
 
< 0.1%
4.51
 
< 0.1%
ValueCountFrequency (%)
3661
 
< 0.1%
3651
 
< 0.1%
3641
 
< 0.1%
3631
 
< 0.1%
3572
0.1%
3561
 
< 0.1%
3552
0.1%
3521
 
< 0.1%
3512
0.1%
3503
0.1%

freq_purchase
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1225
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04969445942
Minimum0.005449591281
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-18T15:24:11.750880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.008746355685
Q10.01578947368
median0.0243902439
Q30.04166666667
95-th percentile0.1153846154
Maximum17
Range16.99455041
Interquartile range (IQR)0.02587719298

Descriptive statistics

Standard deviation0.3374125243
Coefficient of variation (CV)6.789741316
Kurtosis2299.003285
Mean0.04969445942
Median Absolute Deviation (MAD)0.01069161377
Skewness46.11035601
Sum138.0015138
Variance0.1138472115
MonotonicityNot monotonic
2021-10-18T15:24:11.850095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.062518
 
0.6%
0.0277777777817
 
0.6%
0.0238095238116
 
0.6%
0.0833333333315
 
0.5%
0.0909090909115
 
0.5%
0.0344827586215
 
0.5%
0.0294117647114
 
0.5%
0.0192307692313
 
0.5%
0.0256410256413
 
0.5%
0.0212765957413
 
0.5%
Other values (1215)2628
94.6%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
0.1%
0.005665722381
 
< 0.1%
0.0056818181822
0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
171
 
< 0.1%
31
 
< 0.1%
21
 
< 0.1%
1.1428571431
 
< 0.1%
18
0.3%
0.751
 
< 0.1%
0.66666666673
 
0.1%
0.5508021391
 
< 0.1%
0.53351206431
 
< 0.1%
0.53
 
0.1%

qtd_returned
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct204
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.92401873
Minimum0
Maximum9014
Zeros1484
Zeros (%)53.4%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-18T15:24:11.954032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39
95-th percentile96.4
Maximum9014
Range9014
Interquartile range (IQR)9

Descriptive statistics

Standard deviation290.507711
Coefficient of variation (CV)8.318278413
Kurtosis572.5647366
Mean34.92401873
Median Absolute Deviation (MAD)0
Skewness21.64144035
Sum96984
Variance84394.73018
MonotonicityNot monotonic
2021-10-18T15:24:12.048492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01484
53.4%
1129
 
4.6%
2118
 
4.2%
382
 
3.0%
472
 
2.6%
663
 
2.3%
555
 
2.0%
1245
 
1.6%
839
 
1.4%
938
 
1.4%
Other values (194)652
23.5%
ValueCountFrequency (%)
01484
53.4%
1129
 
4.6%
2118
 
4.2%
382
 
3.0%
472
 
2.6%
555
 
2.0%
663
 
2.3%
738
 
1.4%
839
 
1.4%
938
 
1.4%
ValueCountFrequency (%)
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33321
< 0.1%
28781
< 0.1%
20221
< 0.1%
20121
< 0.1%
17761
< 0.1%
15941
< 0.1%

freq_returns
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct424
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2604033399
Minimum0
Maximum4
Zeros1484
Zeros (%)53.4%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-18T15:24:12.144767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.2857142857
95-th percentile1
Maximum4
Range4
Interquartile range (IQR)0.2857142857

Descriptive statistics

Standard deviation0.4445423736
Coefficient of variation (CV)1.707130076
Kurtosis2.092084746
Mean0.2604033399
Median Absolute Deviation (MAD)0
Skewness1.48297621
Sum723.140075
Variance0.1976179219
MonotonicityNot monotonic
2021-10-18T15:24:12.252748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01484
53.4%
1665
23.9%
210
 
0.4%
0.025641025647
 
0.3%
0.57
 
0.3%
0.28571428577
 
0.3%
0.256
 
0.2%
0.0094786729865
 
0.2%
0.22222222225
 
0.2%
0.019607843145
 
0.2%
Other values (414)576
 
20.7%
ValueCountFrequency (%)
01484
53.4%
0.0055710306411
 
< 0.1%
0.0056818181821
 
< 0.1%
0.0058651026391
 
< 0.1%
0.0059347181011
 
< 0.1%
0.0059523809521
 
< 0.1%
0.0060240963861
 
< 0.1%
0.0060422960731
 
< 0.1%
0.0061728395061
 
< 0.1%
0.0061919504641
 
< 0.1%
ValueCountFrequency (%)
41
 
< 0.1%
31
 
< 0.1%
210
 
0.4%
1665
23.9%
0.751
 
< 0.1%
0.66666666673
 
0.1%
0.57
 
0.3%
0.42857142861
 
< 0.1%
0.44
 
0.1%
0.33333333331
 
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1939
Distinct (%)69.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean231.4362258
Minimum1
Maximum6009.333333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-18T15:24:12.360304image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45
Q1103.3333333
median172
Q3278.2
95-th percentile585.1
Maximum6009.333333
Range6008.333333
Interquartile range (IQR)174.8666667

Descriptive statistics

Standard deviation261.5699806
Coefficient of variation (CV)1.130203276
Kurtosis115.6198866
Mean231.4362258
Median Absolute Deviation (MAD)81
Skewness7.719257268
Sum642698.399
Variance68418.85475
MonotonicityNot monotonic
2021-10-18T15:24:12.458010image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10011
 
0.4%
869
 
0.3%
758
 
0.3%
608
 
0.3%
2087
 
0.3%
1367
 
0.3%
1057
 
0.3%
737
 
0.3%
1977
 
0.3%
827
 
0.3%
Other values (1929)2699
97.2%
ValueCountFrequency (%)
11
< 0.1%
3.3333333331
< 0.1%
5.3333333331
< 0.1%
5.6666666671
< 0.1%
6.1428571431
< 0.1%
7.51
< 0.1%
91
< 0.1%
9.51
< 0.1%
111
< 0.1%
11.8751
< 0.1%
ValueCountFrequency (%)
6009.3333331
< 0.1%
3868.651
< 0.1%
28801
< 0.1%
2733.9444441
< 0.1%
2518.7692311
< 0.1%
2160.3333331
< 0.1%
2082.2258061
< 0.1%
20001
< 0.1%
1903.51
< 0.1%
1866.9333331
< 0.1%

avg_basket_variety
Real number (ℝ≥0)

HIGH CORRELATION

Distinct897
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.14209043
Minimum0.2
Maximum177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.8 KiB
2021-10-18T15:24:12.560260image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q17.545454545
median13.5
Q322
95-th percentile45.05
Maximum177
Range176.8
Interquartile range (IQR)14.45454545

Descriptive statistics

Standard deviation14.25442928
Coefficient of variation (CV)0.8315455655
Kurtosis10.0194746
Mean17.14209043
Median Absolute Deviation (MAD)6.666666667
Skewness2.247023856
Sum47603.58512
Variance203.1887541
MonotonicityNot monotonic
2021-10-18T15:24:12.663987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
834
 
1.2%
1333
 
1.2%
932
 
1.2%
1632
 
1.2%
732
 
1.2%
1230
 
1.1%
1429
 
1.0%
629
 
1.0%
1729
 
1.0%
18.529
 
1.0%
Other values (887)2468
88.9%
ValueCountFrequency (%)
0.21
 
< 0.1%
0.253
 
0.1%
0.33333333336
0.2%
0.41
 
< 0.1%
0.40909090911
 
< 0.1%
0.512
0.4%
0.54545454551
 
< 0.1%
0.55555555561
 
< 0.1%
0.57142857141
 
< 0.1%
0.61764705881
 
< 0.1%
ValueCountFrequency (%)
1771
< 0.1%
1051
< 0.1%
1041
< 0.1%
981
< 0.1%
95.51
< 0.1%
94.333333331
< 0.1%
93.333333331
< 0.1%
89.6251
< 0.1%
871
< 0.1%
85.666666671
< 0.1%

Interactions

2021-10-18T15:24:07.872033image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:50.390581image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:51.661185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:52.989244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:54.251975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:55.623768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:56.907696image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:58.341550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:59.655940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:00.893864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:02.405609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:03.754642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:05.009928image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:06.583409image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:07.962653image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:50.503813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:51.745216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:53.071884image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:54.338205image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:55.714279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:56.996983image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:58.430410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:59.739046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:00.999965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:02.497821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:03.839623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:05.099440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:06.670819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:08.051559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:50.589243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:51.826855image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:53.155671image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:54.425348image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:55.803259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:57.087072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:58.518102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:59.820859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:01.087765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:02.592298image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:03.921072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:05.191467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:06.760616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:08.145509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:50.674283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:51.912422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:53.238325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:54.512955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:55.891779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:57.174855image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:58.605602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:59.902144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:01.327461image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:02.681528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:04.001104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:05.279506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:06.850698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:08.236061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:50.762614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:52.001955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:53.328017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:54.605449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:55.983354image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:57.269578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:58.700303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:59.988623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:01.423524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:02.781617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:04.089295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:05.375658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:06.943238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:08.333152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:50.851826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:52.092926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:53.424916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:54.796777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:56.076875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:57.365889image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:58.795921image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:00.076833image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:01.517370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:02.881485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:04.192420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:05.489676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:07.040445image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:08.430152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:50.942611image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:52.183377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:53.516822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:54.891936image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:56.171408image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:57.460538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:58.896718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:00.168236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:01.615408image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:03.009931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:04.282871image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:05.594310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:07.137001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:08.528228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:51.034420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:52.359564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:53.608723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:54.986269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:56.267062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:57.684115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:58.993820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:00.260310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:01.713834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:03.105780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:04.383741image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:05.709607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:07.232051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:08.611766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:51.117638image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:52.441243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:53.697330image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:55.070733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:56.350504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:57.771262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:59.082312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:00.339840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:01.800139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:03.195296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:04.465117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:05.993270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:07.314464image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:08.703537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:51.210638image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:52.530708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:53.791773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:55.161900image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:56.442184image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:57.865577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:59.176977image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:00.429420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:01.906302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:03.295579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:04.557728image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:06.092654image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:07.405546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:08.796540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:51.313612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:52.620347image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:53.885194image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:55.257247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:56.534796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:57.960531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:59.279273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:00.518563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:02.014075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:03.393039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:04.651218image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:06.207896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:07.497788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:08.881276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:51.394354image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:52.700932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:53.973598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:55.342191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:56.620032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:58.051247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:59.366994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:00.609919image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:02.106417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:03.477618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:04.732817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:06.300708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:07.581407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:08.978413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:51.483999image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:52.793444image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:54.075577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:55.437295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:56.717057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:58.149700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:59.464172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:00.709599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:02.215974image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:03.573385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:04.824102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:06.397421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:07.685291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:09.070491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:51.573302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:52.887405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:54.164521image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:55.531589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:56.806774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:58.241807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:23:59.557919image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:00.805281image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:02.306443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:03.663173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:04.917130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:06.488837image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-18T15:24:07.780175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-10-18T15:24:12.766173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-18T15:24:12.954859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-18T15:24:13.099211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-18T15:24:13.237929image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-18T15:24:09.284379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-18T15:24:09.477634image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysqnt_purchasesvar_productsqnt_itemsavg_ticketavg_recency_daysfreq_purchaseqtd_returnedfreq_returnsavg_basket_sizeavg_basket_variety
00178505391.21372.034.0297.01733.018.1522221.00000017.00000040.01.00000050.9705880.617647
11130473232.5956.09.0171.01390.018.90403552.8333330.02830235.00.023973154.44444411.666667
22125836705.382.015.0232.05028.028.90250026.5000000.04032350.00.105263335.2000007.600000
3313748948.2595.05.028.0439.033.86607192.6666670.0179210.00.00000087.8000004.800000
4415100876.00333.03.03.080.0292.00000020.0000000.07317122.00.07894726.6666670.333333
55152914623.3025.014.0102.02102.045.32647126.7692310.04011529.00.032468150.1428574.357143
66146885630.877.021.0327.03621.017.21978619.2631580.057221399.00.019608172.4285717.047619
77178095411.9116.012.061.02057.088.71983639.6666670.03352041.00.013072171.4166673.833333
881531160767.900.091.02379.038194.025.5434644.1910110.243316474.00.072193419.7142866.230769
99160982005.6387.07.067.0613.029.93477647.6666670.0243900.00.00000087.5714294.857143

Last rows

df_indexcustomer_idgross_revenuerecency_daysqnt_purchasesvar_productsqnt_itemsavg_ticketavg_recency_daysfreq_purchaseqtd_returnedfreq_returnsavg_basket_sizeavg_basket_variety
2767561917290525.243.02.0102.0404.05.14941213.00.1428570.00.0202.00000046.000000
276856281478577.4010.02.03.084.025.8000005.00.3333330.00.042.0000001.000000
2769562917254272.444.02.0112.0252.02.43250011.00.1666670.00.0126.00000050.000000
2770564617232421.522.02.036.0203.011.70888912.00.1538460.00.0101.50000015.000000
2771564717468137.0010.02.05.0116.027.4000004.00.4000000.00.058.0000002.500000
2772565813596697.045.02.0166.0406.04.1990367.00.2500000.00.0203.00000066.500000
27735664148931237.859.02.073.0799.016.9568492.00.6666670.00.0399.50000036.000000
2774568914126706.137.03.015.0508.047.0753333.00.75000050.01.0169.3333334.666667
27755695135211092.391.03.0435.0733.02.5112414.50.3000000.00.0244.333333104.000000
2776570515060301.848.04.0120.0262.02.5153331.02.0000000.00.065.50000020.000000